Method for performing segmentation in an ordered sequence of digital data

ABSTRACT

A method for making a segmentation in the sorted sequence of data (100), that comprises arranging a sequential stream of sorted input data (10), where the data are arranged in matrices (frames), in this case by way of a preferred example as a sequence of images for image analysis (20), and that delivers a sequential stream of sorted output data (30), where the sorted output data are arranged in sorted matrices.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation Application of PCT/CL2017/050039,filed Aug. 10, 2017, which claims priority to Chilean Application No.201602047, filed Aug. 12, 2016, the contents of which are herebyincorporated by reference in its entirety.

SCOPE OF THE APPLICATION

The present invention pertains to the detection of elements within asorted sequence of data, such as an image or a data matrix, audio orvideo data. More specifically, a method for performing a segmentation ina sorted sequence of data.

DESCRIPTION OF THE PRIOR ART

At present, industrial activity requires better control tools to controlits processes, to which end real-time image analysis of an onlineprocess is an essential tool for controlling failures and increasingefficiency; for example, a fleet of autonomous mining vehicles must haveoptimal control of their environment in order to be able to deploy suchvehicles, meaning that the processing of work contour images must yieldrelevant information for the fleet's movement and operation; large scaleplantations also need to have timely information as to the status of theplants, so an aerial image is essential for assessing them, while beingable to detect the fruit's quality and health conditions. It is evidentthat digital image analysis is a tool that allows for greater efficiencyin a wide variety of industrial processes.

Patent application US 2012207376 dated Aug. 16, 2012 by Garrote et al,entitled “Bioinspired System for Image Processing,” describes a methodfor processing a bioinspired digital image and includes an architecturethat emulates the functions of a primate's photoreceptors, horizontalcells, bipolar cells and retinal ganglion cells based on an image asinput. The method detects the edges and surface properties present inthe digital image. The output is a dataset that includes photoreceptoremulators that emulate the photoreceptor cells, and is connected to thedata input. Each emulator includes a cell-based structure with amodulated data input, a computing center to process the modulated dataand an output of the data processed by the computing center, andemulators that form a virtual retina in which each emulator isparametrized.

Patent application US 2002168100 dated Jan. 14, 2002, by Roger Woodallentitled “Spatial Image Processor” describes a neural network for aspatial image processor for processing image data to discriminatebetween first and second spatial configurations of the componentobjects, that includes an input matrix to convert an input image intopixel data and send the data to a localized gain network (LGN) module, amemory in parallel with a processor and a neuronal arrangement toreceive the pixel data and the processing of the pixel data in componentrecognition vectors and chaotic oscillators to receive the recognitionvectors and to send the feedback data to the LGN module as attentionactivations. The network also includes a spatial-temporal retina toreceive both the pixel data and the temporary feedback activations andto generate temporary space vectors, which are processed by a temporaryparallel processor in temporary component recognition vectors. A matrixof spacial recognition vectors receives the recognition vectors from thetemporary component and forms an object representation of the first setof objects that comprise it.

None of the cited documents discloses or teaches a method for performinga segmentation in a sorted sequence of digital data to detect elementswithin said sorted sequence of data that can correspond to an image ordata matrix, audio or video data.

SUMMARY OF THE INVENTION

An object of the invention is to perform a segmentation in a sortedsequence of digital data, which comprises applying a firsttransformation to a first matrix, which is the first of a sequentialsorted data input stream, which has three options: reduction,maintenance, or amplification, which results in obtaining a secondmatrix, wherein the first option of a first reduction transformation,consists in each element of the first matrix being linked in a p:q ratiowith the second matrix, where p elements of the first matrix can beprojected into q elements of the second matrix; the first reductiontransformation is applied in a stationary way, i.e. each sector ofpelements of the first matrix generates an element q in the secondmatrix; the second option of a first maintenance transformationcomprises each element of the first matrix being copied in the secondmatrix; the third option of a first amplification transformationconsists of each element of the first matrix being linked in a p:q ratiowith the second matrix, wherep elements of the first matrix can beprojected into q elements of the second matrix; where each element p ofthe first matrix generates q new elements of the second matrix;application of a second transformation to the second matrix, whichconsists of a truncated ramp function, the result of which is a thirdmatrix; application of a third transformation to the third matrix togenerate a fourth matrix, where the third transformation consists ofapplying a discretized Gaussian filter; where each element of the thirdmatrix generates a new element in the fourth matrix; application of afourth reduction transformation to the fourth matrix to obtain a fifthmatrix; application of a fifth binarization transformation to the fifthmatrix to obtain a sixth matrix; where the binarization transformationconsists of binarizing each element of the fifth matrix to thecorresponding element in the sixth matrix, based on whether theelement's value is greater than or less than 50% of the maximum value ofthe fifth matrix, which comprises bringing a 1 or 0 value of the fifthmatrix's element to the corresponding element in the sixth matrix, basedon whether the value of the element is greater than or less than 50% ofthe maximum value of the fifth matrix; applying a sixth reductiontransformation matrix to the sixth matrix to obtain a seventh matrix;applying a seventh transformation to the seventh matrix to obtain aneighth matrix, which consists of binarizing each element in the seventhmatrix, which in turn comprises bringing a 1 or 0 value of the seventhmatrix's element to the corresponding element in the eighth matrix,based on whether the value of the element is greater than or less than50% of the maximum value of the seventh matrix; and where the eighthmatrix becomes the output matrix belonging to the sequentially sorteddata output stream, and the information contained in the eighth matrixsequentially sorted data output stream represents the zones or segmentswith information that delimits the elements which constitute the imageor sequentially sorted data output stream. Furthermore, in the firstreduction transformation after previously having determined a maximumvalue, which is the maximum sum among all p sets of elements in thefirst matrix, the p elements in the first matrix which are placed in theq element of the second matrix, and a percentage average is weighted asthe sum of the p values divided by the maximum sum, which is whatobtains the q value. The first reduction transformation is performed foreach sector of elements of the first matrix, which generates an elementin the second matrix, p/2 elements are displaced horizontally or p/2elements are displaced vertically to form the second matrix; inaddition, the first reduction transformation can be performed withoverlap, wherein each of the first matrix's sector of elements, whichgenerate an element in the second matrix, displace an elementhorizontally or an element vertically, to form the second matrix. In oneoption, the first amplification transformation is performed with a p:qamplification ratio; the value of the q element to be generated isdetermined as follows: a p sector of elements from the first matrix istaken to generate pxq new elements in the second matrix; the highestvalue of the p elements is determined, and then the value of each pelement is divided by the highest value and is normalized by 100, andthe value obtained is placed q times in the second matrix'scorresponding sector. The truncated ramp function has input values,where values above 20% of maximum value take on a value of 100 ormaximum; values below 20%, are multiplied by the ramp function, with aslope m=5; so any value between 0 and less than 20 is multiplied by 5,and in this way the value of each element of the second matrix istransformed into a value in the element of the third matrix. Thediscretized Gaussian filter consists of multiplying each of the elementsof the third matrix by the values set in a matrix section of thediscretized Gaussian filter, which generates a new value in eachelement, which is linearly dependent on the original value and onsurrounding elements, the discretized Gaussian filter is modeled by axxy matrix with element values that are dependent on the size of thematrix, and which are obtained experimentally. For the center of the xxymatrix the value is normalized to 1 and the peripheral values used aredefined between 0 and 1, where the discretized Gaussian filter is basedon the formula: z=b*exp(−a*x²−a*y²); where the constants a and b areexperimentally obtained. The binarization is of a positive type, meaningthat if the element's value is greater than 50% of the maximally valuedelement of the fifth matrix, this element becomes 1, and otherwisebecomes 0; and another of the negative type, meaning that if the valueof the element is greater than 50% of the fifth matrix's maximum value,this element becomes 0, and otherwise becomes 1. For the application ofthe binarization, first the maximum value among the fifth matrix'selements is determined, then a pattern of the types of positive ornegative binarization is defined for each element of the fifth matrix,which is performed randomly, allocating 50% to each type ofbinarization, and the seventh binarization transformation is of thepositive type. It is the case that the sequential stream of sorted inputdata (10), may correspond to a sequence of audio or video data or to adigital image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 describes the basic method of the invention.

FIG. 2 describes the method of the invention with its stages.

FIGS. 3A-3E describe a first transformation of the matrices.

FIGS. 4A-4C describe a second transformation of the matrices.

FIGS. 5A and 5B describe a discrete Gaussian filter applied to a thirdmatrix transformation.

FIGS. 6A-6D describe a fifth matrix transformation using binarization.

FIG. 7 describes an applied example of a highway in real time.

FIG. 8 describes an applied example of detecting or segmenting cars ortrucks.

FIG. 9 describes an example of detecting or segmenting people in anenvironment.

FIG. 10 describes an example of the segmentation of flying elements.

FIG. 11 describes the identification of graphic elements of interest.

DESCRIPTION OF A PREFERRED EMBODIMENT

The present invention relates to the detection of elements within asorted sequence of data, such as an image or a digital data matrix ofdigital data, audio or video data, obtained from an external source,such as an image taken with a photographic camera or similar, a videorecorded with a digital video camera or similar, or a digitally recordedaudio file, etc. The detection of such elements within the sortedsequence of data which is obtained is achieved by using a method forperforming a segmentation in the sorted sequence of data (100).

The method for performing a segmentation in the sorted sequence of data(100) carries out the analysis of a matrix as a sorted element or aframe, for example of a digital image, which is capable of deliveringedge detection or contour and segmentation detection of the digitalimage or of a sorted sequence of data for its comparison, analysis,segmentation, classification, etc.

Each matrix that makes up an elements structure e(i,j,k) that representthe elements of each matrix, where i is an integer value between 1 and Mthat represents the horizontal position within the matrix, and j is aninteger value between 1 and N that represents the vertical positionwithin the matrix, and k is an integer value between 1 and P thatrepresents the depth within the matrix; this is a digital structurewhose value represents a pixel of information or a quantification of theinformation obtained from an external source and stored at a depth of“n” bits, for example 1, 2, 4, 8, 12, 16; preferably values of 1, 8 and12 bits are used for the information stored in the data matrix obtainedfrom an external source.

The segmentation method consists of obtaining chunks of the originalsequence with information based on user-defined parameters. In anexample case, in a digital image of a landscape the zones which delimita tree, a mountain, the sky, a road, etc. can be delivered; in anexample of a sequence of audio data, chunks of audio corresponding to aparticular voice, or of a sound from a specific source (an animal, wind,etc.) can be segmented. In both cases the analysis can be performedeither directly or through a mathematical relationship to the input,transforming the original data into a new input.

FIG. 1 shows schematically the method for performing a segmentation inthe sorted sequence of data (100) that comprises arranging a sequentialstream of sorted input data (10), where the data are arranged inmatrices (frames). In this case of a preferred example, as a sequence ofimages for image analysis (20) and that delivers a sequential stream ofsorted output data (30), where output data are arranged in sortedmatrices.

FIG. 2 depicts the method's flowchart (20) for performing a segmentationin the sorted sequence of data (100) with its stages; the sequentialstream of sorted input data (10), which is sorted in a sequence of datamatrices mxnxp, where m is an integer value greater than 100 thatrepresents the width of the input data of each data matrix, n is aninteger value greater than 100 that represents the height of the inputdata of each data matrix, and p is an integer value greater than orequal to 1 that represents the depth information of the input data ofeach data matrix. In a preferred example, values of 320×240, 640×480,800×600, 1280×960×1, 1680×1050×1, 1920×1280×1 or other image or sortedsequence of data configurations are used. In this way the sequentialstream of sorted input data (10) represents a sequence of data matricesmxnxp where each data matrix is sequentially analyzed.

The first stage begins with a matrix (11), which is the first of thesequential streams of sorted input data (10), a first transformation isapplied to said matrix (11). The transformation offers three options:reduction, maintenance or amplification, and the result of thetransformation returns a matrix (12). The first option of a firstreduction transformation consists of each element of the matrix (11)being linked in a p:q ratio with the matrix (12), where p matrixelements (11) can project themselves into q matrix elements (12); ingeneral, typical matrix (11) values are: 320×240, 640×480, 800×600,1024×720, 1280×960, 2560×1980, or any value of A×B greater than 100×100;in a preferred mode, matrix (11) with dimensions of 1280×960×1 isprocessed in a ratio of p=4:q=1 to obtain a matrix (12) with dimensionsof 640×480×1; the reduction ratio of p=4:q=1 affects the width andheight, while the depth is retained; in a preferred mode it is held thateach side was reduced by factor of 2, going from dimensions of1280×960×1 to dimensions of 640×480×1, while maintaining the depth; i.e.from 4 matrix elements (11) we arrive at 1 element in matrix (12); thisis due to the geometric relationship of a quadrilateral area, where byreducing each side by half the total area is reduced to one quarter ofthe original area; FIG. 3A schematically shows a sector of elements inthe matrix (11) which are placed in the matrix (12), with a 4:1reduction; FIG. 3B shows, by way of example, the values 0, 16, 16, 40,of a sector of the matrix (11) that become a single value 23; previouslya maximum value is determined, which is the maximum sum among all of the4 element sets, as is shown in FIG. 3B, the maximum sum set is 100, 80,80, 60, which yields a maximum value of 320; to arrive at the value of23 as a matrix element (12), an average percentage is weighted as thesum of the values 0, 16, 16, 40, divided by the maximum sum 320, therebyarriving at the value of 23. The first reduction transformation isapplied in a stationary manner, i.e. each four elements sector of thematrix (11) generates an element in matrix (12); however, a firstreduction transformation with overlap can be generated, which means thateach four element sector of matrix (11), which generates an element inthe matrix (12) where two elements are displaced horizontally and twoelements are displaced vertically to form matrix (12), instead now onlyone element is displaced horizontally and one element is displacedvertically to form matrix (12), whereby the reduction is, in itself,minimal; the values are obtained in the same manner as alreadyindicated. The figure schematically shows the result of performing thefirst reduction transformation with overlap, where a 16 element sectoris reduced to nine elements. The second option of a first maintenancetransformation is that each element of matrix (11) is copied to matrix(12). In a preferred mode, matrix (11) with dimensions of 1280×960×1 istransformed using a p=1:q=1 ratio with matrix (12) of dimensions1280×960×1. The third option of a first amplification transformation,for example using a p=1:q=4 ratio, which thereby returns a ratio frommatrix (11) of dimensions 1280×960×1 to matrix (12) of dimensions2560×1920×1; and consists of each element of data matrix (11) generatingfour new elements of matrix (12). In a preferred mode, both the widthand the height of the matrix (11) of dimensions 1280×960×1, ismultiplied by 2, maintaining the depth; and matrix (12) goes on to havedimensions of 2560×1920×1 whereby an amplification ratio of p=1:q=4 isobtained. The value of the element value to be generated is determinedas follows: a 4 element sector of matrix (11) is taken to generate 16new elements in matrix (12), the highest value of the 4 elements isdetermined, in this case being 71, then the value of each element isdivided by the highest value and is normalized by 100, and the valueobtained is placed in the matrix (12) sector 4 times, as is shown inFIG. 3E. The type of first transformation to apply depends on thesequence's size of and format, depending on the determined objective.For instance, looking for a specific segmentation allows for reducedprocessing time, so the user must evaluate which of the three options toadopt.

The second stage begins with a matrix (12), obtained with one of themethods described above; a second transformation is then applied to saidmatrix (12), an example of which is shown in FIG. 4A, consisting of atruncated ramp function, as shown in FIG. 4B, which returns a matrix(13). The truncated ramp function has input values, where values above20% of maximum value take on a value of 100 or maximum; values below20%, are multiplied by the ramp function, with a slope m=5; so any valuebetween 0 and less than 20 is multiplied by 5, and in this way the valueof each element of the matrix (12) is transformed into a value in theelement of matrix (13), as shown in FIG. 4C, and whereby the resultingmatrix (13) is the same size as matrix (12).

The third stage begins with a matrix (13), obtained in the previousstage, and a third transformation being applied to matrix (13) wherebymatrix (14) is generated. The third transformation consists of applyinga discretized Gaussian filter, as shown in FIG. 5A, and a transformationand transfer of matrix information is performed. The implementation ofthe Gaussian filter consists of multiplying each of the matrix elements(13) by the values set in the discretized Gaussian filter matrixsection, which generates a new value in each element, said values beinglinearly dependent on the original value and on surrounding elements, asshown in FIG. 5B; in the case of a preferential example, the discretizedGaussian filter is modeled by a 3×3 matrix with element values definedin FIG. 5A. These values are dependent on the size of the matrix and areexperimentally obtained. For the center of the 3×3 matrix a normalizedvalue of 1 will be used, and the peripheral values will be definedbetween 0 and 1. By doing this the discretized Gaussian filter matrixsection is applied, in the same way as the maintenance transformation,whereby matrix (14) is obtained. The Gaussian filter employed is basedon the formula: z=b*exp(−a*x²−a*y²); in the example, the experimentallyobtained constants used are a=0.2 and b=1. By discretizing in a 3×3matrix the discretized Gaussian filter values shown in FIG. 5A areobtained. In a case of wanting to use a 5×5 matrix, this same functioncan be used to obtain its elements. It is important that the matrixdimension be an odd number as this allows for a single center to beobtained in the discretized Gaussian filter matrix.

The fourth stage is begun with the matrix (14) obtained in the previousstage. A fourth transformation is applied to matrix (14) to generatematrix (15). The fourth transformation consists of applying a reductiontransformation to obtain matrix (15), as described in the first stage.

The fifth stage begins with the matrix (15) obtained in the previousstage, and a fifth transformation is applied to matrix (15) and matrix(16) is generated; the fifth transformation consists of binarizing eachelement of matrix (15) which entails taking the 1 or 0 value of thematrix (15) element to the corresponding matrix (16) element, based onwhether the element's value is greater or less than 50% of the maximummatrix (15) value. 2 types of binarizations are defined. One is type A,or positive, which corresponds to whether the element's value is greaterthan 50% of the maximum matrix (15) value, in which case it becomes a 1,or otherwise becomes 0. Type B or negative binarization works asfollows: if the element value is greater than 50% of the matrix's (15)maximum value, this element transforms to 0, and otherwise it becomesa 1. For the application of the described binarization, first themaximum value within the matrix elements (15) is determined, then apattern of the types of A or B binarization for each element of thematrix (15) is defined, which is performed in a random way, allocating50% to each type of binarization. An example of a distribution patternis shown in FIG. 6A, which is generated from a distribution like the oneshown in FIG. 6B, where the previously determined maximum value, whichin this example is 100, is then applied to the distribution in FIG. 6C,and lastly the transformation is performed, which results in the valuesin FIG. 6D; with FIG. 6D being matrix (16).

The sixth stage is begun with the matrix (16) obtained in the previousstage. A sixth transformation is applied to matrix (16) to generatematrix (17). The sixth transformation consists of applying a reductiontransformation to obtain matrix (17), as described in the first andfourth stages.

Note: When applying a reduction transformation which contains binaryelements, and given that the reduction operation used is the sum of theelements, the resulting matrix will not be binary. For example, if wehave a 4:1 transformation, and the first 4 elements of the input matrixare ones, and they are reduced to the first element of the outputmatrix, this value will be 4. Therefore, the resulting matrix will nothave binary elements, and a new binarization must be applied.

The seventh stage begins with the matrix (17) obtained in the previousstage, and a seventh transformation is applied to matrix (17) and matrix(18) is generated; the seventh transformation consists of binarizingeach element of matrix (17) which entails taking a 1 or 0 value of amatrix (17) element to the corresponding matrix (18) element, based onwhether the element's value is greater or less than 50% of the maximummatrix (15) value, where the binarization is of type A, or positive,employed in the fifth stage, which means that if the element's value isgreater than 50% of the maximum matrix (17) value then this elementbecomes 1, and otherwise it becomes 0.

In the previously described method, in matrix (14) to matrix (15) it isheld that each element of these matrices takes a set of elements fromthe previous matrix and relates them in accordance with the p:q ratio,as has been explained in the corresponding stages. In addition, the p:qratio can be performed using an overlap of matrix (14) elements tomatrix (15). The level of overlap is experimentally determined, and canbe greater than or equal to zero. This means that each element canbelong to one or more matrices, for example, the first element of matrix(15), e₂₇(1,1,1), corresponds with 4 elements of matrix (14),e₂₅(1,1,1), e₂₅(1,2,1), e₂₅(2,1,1), e₂₅(2,2,1); the second element ofmatrix (14), e₂₇(1,2,1), corresponds with 4 elements of matrix (15),e₂₅(2,1,1), e₂₅(3,1,1), e₂₅(2,2,1), e₂₅(3,2,1), of which 2 elements areoverlapped, i.e. elements e₂₅(2,1,1), e₂₅(2,2,1), belong to 2 matricesin matrix (15).

Lastly, matrix (18) becomes the output matrix for the sequential streamof sorted output data (30) of Q×R×S dimensions, where Q is an integervalue greater than one that represents the width of the sorted outputdata (30), R is an integer value greater than one that represents theheight of the sorted output data (30), S is an integer value greaterthan or equal to one that represents the depth information of the sortedoutput data (30). In the preferred example, values of 320×240×1 areused. The information contained in matrix (18) of the sequential streamof sorted output data (30) represents the zones or segments withinformation delimiting the constituent elements of the image orsequential stream of sorted output data (30).

Matrix (18) belonging to the sequential stream of sorted output data(30) can be geometrically referenced on the sequential stream of sortedinput data (10), depending on the configuration of the original datastream (linear, two-dimensional, three-dimensional, etc.), whilerespecting the p:q accumulation factors used in the stages describedabove.

Examples of Use

For the analysis of a highway in real time, as shown in FIG. 7, emergingline forms which define the lanes can be detected and used for automatictracking in an autonomous navigation system.

In the case of detecting or segmenting cars or trucks, as shown in FIG.8, they can be permanently monitored within the visual field as with asequential stream of sorted input data (10).

In the case of detecting or segmenting people in an environment, asshown in FIG. 9, peoples' silhouettes can be detected in an emergentform.

In the case of detecting or segmenting elements, both elements ofinterest and floating elements can be detected. FIG. 10 shows how thesilhouette of an airplane, clouds, and the sky/mountain boundary can besimultaneously detected.

In the case of detecting or segmenting symbols within an environment, asshown in FIG. 11, numbers or letters or other graphic elements ofinterest can be differentiated.

Obtaining the edges of the relevant elements in the images is achievedthrough the application of the transformations disclosed in the methodwhich return an emergent output or result. In a comparison withtraditional artificial vision techniques, this method would beequivalent to applying a retinal filter to the images.

The foregoing is based on the fact that in the case of biological eyesnature does it this way, and our model is a method that partiallyreplicated biological characteristics. There is no method that describethe entire process of obtaining edges, as the results are obtainedthrough the multiple transformations described, with these results beingused in obtaining the emerging edge forms. While there are other edgedetection techniques in the traditional artificial vision field theyrequire high levels of computing power and are not a linear process,unlike the method described herein.

1. A method for performing segmentation in a sorted sequence of digitaldata, characterized in that it comprises: applying to a first matrix(11), which is the first of a sequential stream of sorted input data(10), a first transformation that has three options: reduction,maintenance, or amplification, and as a result of which a second matrix(12) is obtained, where the first option of a first reductiontransformation comprises each element of the first matrix (11) beinglinked in a p:q ratio with the second matrix (12), wherep elements ofthe first matrix (11) con project into q elements of the second matrix(12); the first reduction transformation being applied in a stationaryway, i.e. each sector of p elements of the first matrix (11) generate anelement q in the second matrix (12); the second option of a firstmaintenance transformation comprises each element of the first matrix(11) being copied in the second matrix (12); the third option of a firstamplification transformation comprises each element of the first matrix(11) being linked in a p:q ratio with the second matrix (12), where pelements of the first matrix (11) can project themselves into q elementsof the second matrix (12); where each elementp of the first matrix (11)generates q new elements in the second matrix (12); applying a secondtransformation, consisting of a truncated ramp function, to the secondmatrix (12), which results in obtaining a third matrix (13); applying athird transformation to the third matrix (13) to generate a fourthmatrix (14), where the third transformation comprises the application ofa discretized Gaussian filter; where each element of the third matrix(13) generates a new element in the fourth matrix (14); applying afourth reduction transformation to the fourth matrix (14) to obtain afifth matrix (15); applying a fifth binarization transformation to thefifth matrix (15) to obtain a sixth matrix (16); where the binarizationtransformation comprises binarizing each element of the fifth matrix(15) into the corresponding element in the sixth matrix (16), based onwhether the element's value is greater than or less than 50% of thefifth matrix's (15) maximum value, which comprises taking a 1 or 0 valueof the fifth matrix (15) element to the corresponding element in thesixth matrix (16), based on whether the element's value is greater thanor less than 50% of the maximum fifth matrix's (15) value; applying asixth reduction transformation to the sixth matrix (16) to obtain aseventh matrix (17); applying a seventh transformation to the seventhmatrix (17) to obtain an eighth matrix (18), which comprises binarizingeach element of the seventh matrix (17) to take a 1 or 0 value of theseventh matrix (17) element to the corresponding element in the eighthmatrix (18), based on whether the element's value is greater than orless than 50% of the maximum seventh matrix's (17) value; and where theeighth matrix (18) becomes the output matrix for the sequential streamof sorted output data (30), and the information contained in the eighthmatrix (18) of the sequential stream of sorted output data (30)represents the zones or segments containing information delimiting theimage's constituent elements, or the sequential stream of sorted outputdata (30).
 2. The method according to claim 1, characterized in that inthe first reduction transformation a maximum value is previouslydetermined, which is the maximum sum among all the sets of p elements inthe first matrix (11), the p elements in the first matrix (11) which areplaced in the q element of the second matrix (12), and a percentageaverage being weighted as the sum of thep values divided by the maximumsum, to obtain the value of q.
 3. The method according to claim 1,characterized in that the first reduction transformation is performedfor each element sector of the first matrix (11), which generates anelement in the second matrix (12), which displaces p/2 elementshorizontally or p/2 elements vertically to form the second matrix (12).4. The method according to claim 1, characterized in that the firstreduction transformation is performed with overlap, where each elementsector of the first matrix (11), which generates an element in thesecond matrix (12), displaces one element horizontally or one elementvertically to form the second matrix (12).
 5. The method according toclaim 1, characterized in that the first amplification transformationhas a p:q amplification ratio; the value of the q element to begenerated being determined as follows: a p sector of elements from thefirst matrix (11) is taken to generate pxq new elements in the secondmatrix (12); the highest value of the p elements is determined, then thevalue of each p element is divided by the highest value and isnormalized by 100, and the value obtained is placed q times in thesecond matrix's (12) corresponding sector.
 6. The method according toclaim 1, characterized in that the truncated ramp function has inputvalues, where values above 20% of maximum value take on a value of 100,or maximum; values below 20% are multiplied by the ramp function, with aslope m=5; so any value between 0 and less than 20 is multiplied by 5,and in this way the value of each element of the second matrix (12) istransformed into a value in the element of the third matrix (13).
 7. Themethod according to claim 1, characterized in that the discretizedGaussian filter comprises multiplying each of the elements in the thirdmatrix (13) by the values set in a matrix section of the discretizedGaussian filter, which generates a new value in each element, which islinearly dependent on the original value and on surrounding elements;the discretized Gaussian filter is modeled by an xxy matrix with elementvalues that are dependent on the size of the matrix, and which areobtained experimentally. For the center of the xxy matrix the valuenormalized to 1 is used, and the peripheral values are defined between 0and 1, where the discretized Gaussian filter is based on the formula:z=b*exp(−a*x²−a*y²); where the constants a and b are experimentallyobtained.
 8. The method according to claim 1, characterized in that thebinarization is of a positive type, meaning that if the element's valueis greater than 50% of the fifth matrix's (15) maximum-value element,this element becomes 1, and otherwise becomes 0; and another of thenegative type, where if the element's value is greater than 50% of thefifth matrix's (15) maximum value, this element becomes 0, and otherwiseit becomes
 1. For the application of the binarization, first the maximumvalue within the elements of the fifth matrix (15) is determined, andthen a pattern of the types of positive or negative binarizations isdefined for each element of the fifth matrix (15), which is performedrandomly, allocating 50% to each type of binarization.
 9. The methodaccording to claim 1, characterized in that the seventh binarizationtransformation is of the positive type.
 10. The method according toclaim 1, characterized in that the sequential stream of sorted inputdata (10) may comprise a sequence of audio or video data or a digitalimage.